A voter deposits a mail-in ballot in a drop box in Chester County, Pa., on Nov. 5, 2024. The U.S. Department of Justice announced Thursday it is suing Pennsylvania and five other states that have refused to turn over detailed voter roll data demanded by federal attorneys earlier this year. (Photo by Peter Hall/Pennsylvania Capital-Star)

The U.S. Department of Justice announced Thursday it is suing six states — California, Michigan, Minnesota, New York, New Hampshire and Pennsylvania — that have refused to turn over detailed voter roll data demanded by federal attorneys earlier this year.

The Justice Department has reached out to more than half the states in recent months for voter lists, and has indicated it plans to contact all of them. Some of the requests vary in detail, but in general they ask for voter information on millions of Americans, including personal data such as driver’s license numbers and partial Social Security numbers.

Some states have released only publicly available data or invited DOJ attorneys to make public records requests. Others have refused outright. Indiana last week became the first known state to have provided sensitive personal data.

Under the Constitution, states are responsible for administering elections, and some state election officials have said they are barred by state law from handing over the information the Justice Department has demanded. The U.S. Department of Homeland Security this month confirmed it had received data from the Justice Department and would use it unearth “illegal aliens.”

The Trump administration also is developing a powerful data tool that it says will help states prevent noncitizens from voting, which is extremely rare.

The lawsuits have been filed in the federal districts of the respective states. They argue that the federal government is privy to the data under two federal laws, the Help Americans Vote Act (HAVA) and the National Voter Registration Act (NVRA), that were passed “to ensure that states have proper and effective voter registration and voter list maintenance programs,” the Justice Department said in a news release.

“Clean voter rolls are the foundation of free and fair elections,” said U.S. Attorney General Pam Bondi in a statement announcing the lawsuits. “Every state has a responsibility to ensure that voter registration records are accurate, accessible, and secure — states that don’t fulfill that obligation will see this Department of Justice in court.”

Stateline editor Barbara Barrett can be reached at bbarrett@stateline.org

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This is Hongyu Liu, Wisconsin Watch’s new data investigative reporter. 

If you’ve ever been confused and even intimidated by statistics and other numbers, I feel you. 

I was in the same boat three years ago while interning at a newspaper in Quincy, Massachusetts. 

When gas prices soared, my colleagues and I made weekly calls to every gas station in town, asking for price updates. It was an effort to help readers who were not familiar with gas price apps to learn where to fill up their cars more affordably. But we were soon drowning in data. Each week, we posted a lengthy list of individual prices, which were already one day old when reaching the readers’ doorsteps. We didn’t quite know how to look at the numbers in a more thoughtful, useful way.

Had I the analysis skills I’ve since developed, I would have approached the assignment differently. I would have looked for trends that may have inspired stories about how the higher gas prices might tighten the budgets of residents. 

My eagerness for understanding the world of numbers prompted me to pursue a master’s degree in data journalism at Columbia University. There, I found my niche is where data analysis, web design and journalistic storytelling intersect. I went on to spend almost two years in Charleston, South Carolina, as a data reporting fellow at The Post and Courier, becoming the newsroom “data nerd.” I used data skills to sift through drug prescription records to find evidence of identity theft and understand how loosened regulations led to a surge in sea turtle deaths from dredging near ports.

Now I’m eager to do similar reporting for Wisconsin, using data to provide rich context to our journalism that aims to make communities strong, informed and connected. That includes finding investigative leads rooted in data and producing visualizations that explain the issues we cover, such as through our DataWatch series.

We live in a world with ever-increasing reams of raw data that, if understood and analyzed, can help us better understand our communities. I’m stepping in at Wisconsin Watch to take the lead on how we use data in our journalism — and to understand how actors across the state are representing or even misrepresenting data trends.

I want to hear from you. If you have ideas for data we should analyze and visualize, or if you have questions about data in a government report, email me at hliu@wisconsinwatch.org and share your thoughts.

Digging into data that explains Wisconsin is a post from Wisconsin Watch, a non-profit investigative news site covering Wisconsin since 2009. Please consider making a contribution to support our journalism.

Now that you’ve made it through the beginning of the new school year, this is the optimal time to tune your transportation operations.  Learn how to leverage your live ridership, routing, and call volume data to reduce missed stops, decrease parent inquiries, and enhance on-time performance.

Join Pathwise and School Transportation News on Thursday, October 9, at 10:00 a.m. PT / 1:00 p.m. ET for a live 60-minute session to discover how to convert your early-semester data into concrete mid-year gains without replacing your current routing platform.

In this session, you’ll get practical tips on how to:

• Reassess your routes using current ridership signals (scan data, driver logs, parent app activity, no-show patterns)
• Improve routing reliability with targeted fixes for tier balancing, stop consolidation, bell-time alignment, and more, instead of major re-routes
• Track ridership more accurately using count audits, exception workflows, and reconciliations, and turn these insights into schedule improvements
• Operationalize KPIs that matter—on-time percentage, call-center volume, and parent notification latency—so you know where adjustments may need to be made.

Bottom line: This isn’t starting over; it’s making smarter use of tools, data, and processes you already have—to ensure smoother operations throughout the rest of the year.

Who should attend: Transportation directors, routing/dispatch leads, and operations managers.

Brought to you by Pathwise

REGISTER BELOW:

 

Presenters:

Michael Roche
VP of Customer Engagement and Business Development
EZRouting

With over 13 years of experience as a Director of Transportation for a school district, Roche possesses extensive expertise in overseeing logistical operations and ensuring the safety and efficiency of transportation systems. Transitioning into consulting, he has utilized his knowledge to aid school districts in optimizing transportation operations and implementing software solutions. Currently, Roche is committed to collaborating with school districts across the country, assisting them in maximizing the benefits of the software and providing comprehensive consulting services tailored to their transportation requirements.

Carl Allen
Chief Executive Officer
4MATIV

Carl Allen is an experienced leader in education, transportation, and public policy, currently serving as CEO and founder of 4MATIV Technologies, which he launched in 2018. He previously served as Director of Transportation for Boston Public Schools, Regional Vice President for Transdev in Colorado, and COO/CFO of a charter school network in Minneapolis. Drawing on his training in urban planning and public policy from Harvard’s Kennedy School, and his early experience as a Peace Corps Volunteer teaching high school math in Ghana, Allen supports school districts in tackling complex transportation challenges. He holds degrees in industrial and manufacturing design engineering from Northwestern University and lives in St. Paul, Minnesota with his wife and three children.

The post (Free Webinar) From First Day to Fine-Tuning: Optimizing Your Transportation Operations Following the Return to School appeared first on School Transportation News.

Hundreds of thousands of students are on new routes to and from school this month.
While some school districts may still be tracking these numbers manually, many
transportation departments are implementing new technology to take the guesswork out of student ridership.

Luisa Brown is wearing two hats at Zillah School District in Washington, that of an accounts payable supervisor and transportation manager. When she started in the latter role in March 2020, and without a long background in student transportation, she leaned heavily on technology for all the assistance she could get.

Brown said that despite working at a smaller school district that transports approximately 662 students daily, she realized that tracking routing via spreadsheets was not an ideal solution. That’s when she first started using the Tyler Technologies routing software, implemented in December 2020. The student ridership verification
technology via RFID student cards was added in 2023.

A phased approach to implementing new technology was necessary from a budgetary standpoint, she noted, which also was essential for ensuring the technology is utilized correctly and benefitting the student transportation staff.

Tim Ammon, a consultant in the student transportation industry since 2001, said the “Holy Grail” of this kind of technology is the amount of intervention required.
Ammon explained that in his experience as a consultant and working in the business management of school bus technology (he recently served as VP and GM of passenger services for Zonar Systems and remains a strategic advisor), he sees two main uses of student ridership verification.

“The first is, in the event that something goes wrong, we can track back to where the kid got on and off the bus and at least have a starting point. So, emergency district management applications.”

In Brown’s case, integration was smooth, since she said she was already using Tyler’s routing software and Tyler Drive to connect with the RFID cards. But in Colorado, Denver Public Schools (DPS) ran into challenges as transportation prepared to roll out student ridership technology last month for the first time.

“Samsara has been a very willing and helpful partner in making sure all the components of our project roll-out smoothly and are operational internally,” said Tyler Maybee, director of operations for Denver’s transportation services, who said the district is creating an in-house student ridership technology solution with the GPS provider alongside a smaller technology company.

“We have another vendor that is more of a barrier than opportunistic and has prevented our innovation from raising the bar within their own technology. It has forced us to find many workarounds and begin to search for a better partner that has a similar vision to fully integrate transportation technology.”

With about 5,000 to 7,000 students being transported daily across Denver, Maybee said time will tell the success of the new project.

“But all signs point to a more knowledgeable and connected DPS community and a reduction in the number of calls our dispatchers receive regarding missing students and requests for bus information,” he said.

Keeping Data Secure
On the topic of data security for this type of technology, Ammon noted it’s crucial to have “procedural aspects in place to make sure that you know that information is
protected.” Easier said than done as it’s a process that can have an “enormous number of tentacles into it,” he added.

An Education Week article found that education was the fourth-most targeted sector during the first half of 2025, based on data collected by Comparitech.

“Schools are tempting targets for hackers because they have tons of sensitive data and have become more reliant than ever on digital tools,” the article stated. Amy McLaughlin, the project director for the Consortium for School Networking’s (CoSn) cybersecurity initiatives, was quoted saying that districts are aware of the security concerns but face challenges of funding and staff to ensure that data and cybersecurity issues are adequately addressed.

Brown said she keeps physical security on a tight lockdown as each tablet has a unique PIN that only she and the individual driver has access to.

Bill Westerman, Tyler’s director of integration solutions, confirmed that all Tyler Drive tablets are encrypted and that districts can choose how registration information is shown when student data is being inputted.

Maybee said the Denver IT team has a series of regulations in place to prevent student data from falling into the wrong hands and that vendors are required to sign a data privacy agreement “to make sure their systems meet the same level of security our network has to maintain adequate protections,” he continued. “We limited the amount of personal identifiable information on the ID virtual and physical ID cards to make sure even if a card was misplaced and then subsequently found that a student’s information is not at risk. This also includes encrypting the QR code so that a scan must be tied to our system to make any sense out of the resulting scan data.”

Edulog’s Lam-Nyugen Bull, who serves as the company’s chief experience officer, said the software company maintains SOC 2, Type 2 compliance and that “all data is encrypted at rest and in transit and we regularly undergo third-party penetration testing and evaluation of our overall security posture.”

As a certified risk manager, Ammon encouraged student transportation professionals to find resources or individuals that can assist with being able to “talk to your vendor intelligently about their data security procedures.”

Especially when integrating different vendors’ technology options into one transportation operation, he said that collaboration is crucial with increased risk of
malicious cybersecurity attacks.

“From a vendor perspective, it’s very likely that each district will have its own flavor of how it wants to deal with this, and so like as a vendor, I should know that,
right? Because I should be responding to what your requirements are as a customer, right? To assume that all 16,000 school districts in the country want exactly the same response in the event of it is, I think, a fallacy,” Ammon said. “There should be some collaboration between the district and the vendor in terms of, here’s our expectations around this, here’s the universe of what’s possible. How do we want to narrow that universe so that it fits whatever we’re doing?”

Evolving Technology
RFID cards, QR codes, barcodes and manual checklists are all ways that student ridership can be documented. Most industry experts agree that RFID cards can help keep tabs on the students on the bus without exposing their information, but what are the future possibilities when it comes to this technology?

Ammon noted that video camera facial recognition or biometric scans are trickier territory to navigate as those types of technology naturally raise a high level of privacy concerns with parents.

“There is no technology impediment today that would stop us from doing [options like biometric scanning],” said Zach Moren, Transfinder’s manager of sales enablement and engineering. “But schools need to consider a few things when looking at ridership solutions. What is the most cost effective? What is the most reliable to capture as close to 100 percent of riders as possible? And what technology can be easily adopted and utilized by bus drivers, students and the community? Based on those requirements I’m skeptical we will see a major change in technology anytime in the near future because RFID solves each of those challenges so effectively.”

Moren noted that Transfinder is developing a digital wallet card that students could access on their smartphones, “like they would a credit card or concert ticket,” which Moren said could address the issue of RFID cards being lost or damaged.

“As schools continue to prioritize student well-being, the evolution of ridership verification technology is set to move beyond isolated solutions and adopt a more holistic approach, intertwining safety and health measures with the core mission of ensuring every child’s secure passage to and from school,” said Edulog’s Nyugen-Bull
when discussing the future of this technology.

Brown noted that one Tyler software feature she found to be immensely helpful is the ability to run health reports to make sure drivers were aware of health information for the students on their routes, such as food allergies or other relevant factors such as anxiety. She said this information was historically kept in a folder or backpack on the bus, which was not the best way for drivers to quickly access the information and be aware of important student information or emergency contact details.

She also noted that Tyler is doing “an amazing job of making updates throughout the year, so that it’s not just a dead program and [it’s] improving every year,” she continued. “And I think they do an amazing job in getting the in-user’s input because they are creating something that they feel is going to work for everybody.

Because there [are] different circumstances in small districts versus large districts.”
Integration and collaboration continue to be important factor for companies and districts as they work together to keep student data secure and improve on the implementation of this technology to benefit not only the students but student transportation operational workings.

Editor’s Note: As reprinted from the September 2025 issue of School Transportation News.

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Related: Ride and Drive, Technology Product Demos Return to Texas in November
Related: Georgia School District Removes Multiple Bus Drivers Over Safety Violations
Related: School Bus Safety Company Unveils New Leadership Training Course to Elevate Safety Leadership
Related: Smart Buses, Smarter Outcomes

The post Transforming Student Ridership appeared first on School Transportation News.

Increasingly, the conversation about cybersecurity and data protection includes student transportation. STN addressed the subject of security in the September magazine issue, featuring articles that focused on video camera storage and security as well as data security and routing.

STN spoke with Jake McOmie, the CTO of Confluence Security, a systems integrator company that brings together products from various manufacturers — of cameras, recording devices, servers, networking equipment, and sensors — to create tailored security systems. These systems are designed to address both physical and cybersecurity needs with an emphasis on automation, identity management and analytics. The company, which works with government, school and commercial or enterprise customers, also provides software that unifies all components, enabling features like real-time alerts, video analytics and automated response to security events.

STN: Why is security and cybersecurity important for school districts and transportation departments right now?

McOmie: Security and cybersecurity aren’t new concerns, but in today’s connected world, they are more critical than ever. School districts are rapidly adopting technologies like IP cameras, GPS systems, Wi-Fi routers and student tracking software. These tools improve safety and efficiency, but each device added to the network also introduces potential vulnerabilities.

We call this security of security, a phrase borrowed from our trusted manufacturer partner of open-architecture security software platform, Genetec. The approach ensures a cybersecurity-first posture and it’s critical practice to understand your product choices are being systemically protected by design, not as an afterthought.

In the age of the Internet of Things (IoT), everything is interconnected. One unsecure device — whether a camera, HVAC sensor, or access control point — can act as the weak link that compromises the entire system. No matter how robust a network may be, its strength depends on every component being secure. That’s why it’s not enough to harden just the network. Districts must vet the products themselves, hold manufacturers accountable for cybersecurity practices and ensure every piece of technology is built with a “security-first” mindset.

Trust is earned, not assumed. Cybersecurity must be woven into procurement, deployment and management. When one compromised camera or device can become an open door, due diligence isn’t optional. It’s essential.

STN: How can transportation departments ensure their data is protected? What steps should they be taking?

McOmie: Transportation departments manage highly sensitive data, including student info, vehicle locations, incident videos and operational logs. To protect this data, a comprehensive approach during the initial planning will ensure this sensitive data is not jeopardized from unauthorized access. We can talk about the various aspects end users should keep forefront during the planning phase

• • Vet manufacturers and integrators. Work only with vendors that prioritize cybersecurity and provide transparent security documentation. Vendors who operate under zero-trust security policies and demand nothing less of their technology partners, should be asked early in the process. It’s a pass or fail question and should be enforced without hesitation.
  • Network segmentation. Isolate transportation and security systems from general-use school networks. Implementing advanced enterprise segmentation through Federations allows for controlled third-party access while maintaining autonomous and isolated authorization. Preferably utilize SaaS-hosted federation services so partner agencies, such as between schools and 911 centers, can connect their networks for data sharing without actually connecting to anything except the mediary cloud-hosted federation server. This method adds the benefit of permission-based access at the most minute level of data, like allowing access to a video feed only if three independent trigger points have verified.
  • Multi-factor authentication (MFA). Implement MFA at all levels — application logins, device portals and cloud platforms — to prevent account takeovers, especially when passwords are compromised.
  • Zero-trust approach. Assume no device or user is secure by default. Require verification and limit access by role. To maximize the effects of this policy, utilize automations and/or integrations to minimize the number of touchpoints when permission changes occur.
  • Encryption & updates. Use end-to-end encryption for data in motion and ensure firmware/software is routinely patched. If available, consider using SaaS products to perform all or some tasks, which can help protect systems from becoming outdated, even if only for a short duration.
  • Automation & alerting. Leverage tools that can automatically identify patterns or anomalies and escalate issues to the right personnel. Open-architecture systems allow for a larger variety of inputs, and with proper configuration, the sensors can be associated with other sensors or events to help qualify any given scenario before notifying personnel, and ensure the correct personnel are the ones being notified.

Protecting data is not just about prevention. It’s about building resilience and ensuring your team can respond quickly and effectively when an event occurs.

STN: How do you advise school districts to work with their technology department?

McOmie: One of the most common challenges we see is operational silos. Safety and security departments know the problems they need to solve, but IT departments hold the keys to implementation. Successful projects require early and continuous collaboration between these teams.

At Confluence Security, we provide end-to-end IP-based solutions, which means we’re deeply engaged with IT teams during planning, design and deployment. While safety leaders define the why, IT ensures the how is executed securely and effectively. The IT team is critical in achieving a successfully hardened system and should include these three key points:

• • Designing the network architecture to limit exposure.
  • Setting access controls and firewall rules.
  • Validating compliance with cybersecurity policies.

In today’s world, a zero-trust model is no longer optional. Every actor, internal or external, must be authenticated and authorized. School districts can support this by standardizing processes like MFA and ensuring IT reviews any new connected hardware or software before it’s deployed.

STN: Where do you see AI in security?

McOmie: AI is transforming security in two important ways — behind the scenes and in front of the user.

Behind the scenes, AI helps devices self-optimize — learning traffic patterns, refining video compression, or detecting performance anomalies before they become problems. This isn’t flashy, but it’s foundational to deliver faster, smarter, more reliable systems. The increased accuracy and performance is generally appreciated by end users but in today’s world of tech, the continual improvements are more or less expected.

Video Analytics engines, where video streams are computer-analyzed for specific behaviors, have used AI to improve their intelligence for more than a decade in some cases. In this method, software developers gain tremendous assistance with perfecting their analytical algorithms. In recent years, advancemnts have been made so far as to providing users with the ability to generate their own behavior definitions and AI creates the behavior analysis, delivering a DIY approach to video analytics.

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Related: Security Sessions at STN EXPO East Address Violence, Safety Programs
Related: As Camera Systems Evolve, IT Collaboration Necessary

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From the user perspective, AI enhances how we interact with security systems. Instead of digging through hours of video, users can issue simple commands: “Show me anything unusual at Bus Lot A last night,” or “Search for students wearing red backpacks on buses 12 thru 15 last week.”

AI enables faster investigations and richer situational awareness. Rather than responding to noise (e.g., constant motion alerts), users receive qualified insights based on anomalies — events that stand out from the norm, like a student jumping out of an open bus window, or a person loitering in an atypical location.

But AI doesn’t stop at behavioral detection. It fundamentally supports action through automation. Systems can support users through if/then/else conditional logic decision making to promote accuracy in the users actions and response. Ultimately, the preferred outcome can be guided by digitized SOPs, allowing for a newbie operator to respond the same way a well-seasoned operator would.

These layers of logic ensure that when serious threats arise, escalation to law enforcement or 911 is intentional, not a false alarm, and delivers real actionable video, data and evidence.

STN: Thank you.

The post Q&A: Cybersecurity in Student Transportation: Why It Matters, Where It’s Headed appeared first on School Transportation News.

The post How concerned are you about the data security of your student transportation operations? appeared first on School Transportation News.

School bus operations rely on technology to address the complex requirements of transporting students. Properly identifying your organization’s operational, functional, technical and financial needs will enhance its capabilities as well as your satisfaction with your choices—but how to start?

Download our complimentary white paper for fresh perspective into choosing a partner who provides good value, not just a good price.

• Take a wide, objective look at your organization to understand what’s needed.
• Identify the new technologies’ impact to end users and other departments.
• Work with potential partners to define KPIs and calculate projected ROI.
• Evaluate providers in detail to determine their suitability as a long-term partner.
• Ensure regulatory compliance, and look for partnerships and integrations.

Fill out the form below and then check your email for the white paper download link.

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On May 6, MIT AgeLab’s Advanced Vehicle Technology (AVT) Consortium, part of the MIT Center for Transportation and Logistics, celebrated 10 years of its global academic-industry collaboration. AVT was founded with the aim of developing new data that contribute to automotive manufacturers, suppliers, and insurers’ real-world understanding of how drivers use and respond to increasingly sophisticated vehicle technologies, such as assistive and automated driving, while accelerating the applied insight needed to advance design and development. The celebration event brought together stakeholders from across the industry for a set of keynote addresses and panel discussions on critical topics significant to the industry and its future, including artificial intelligence, automotive technology, collision repair, consumer behavior, sustainability, vehicle safety policy, and global competitiveness.

Bryan Reimer, founder and co-director of the AVT Consortium, opened the event by remarking that over the decade AVT has collected hundreds of terabytes of data, presented and discussed research with its over 25 member organizations, supported members’ strategic and policy initiatives, published select outcomes, and built AVT into a global influencer with tremendous impact in the automotive industry. He noted that current opportunities and challenges for the industry include distracted driving, a lack of consumer trust and concerns around transparency in assistive and automated driving features, and high consumer expectations for vehicle technology, safety, and affordability. How will industry respond? Major players in attendance weighed in.

In a powerful exchange on vehicle safety regulation, John Bozzella, president and CEO of the Alliance for Automotive Innovation, and Mark Rosekind, former chief safety innovation officer of Zoox, former administrator of the National Highway Traffic Safety Administration, and former member of the National Transportation Safety Board, challenged industry and government to adopt a more strategic, data-driven, and collaborative approach to safety. They asserted that regulation must evolve alongside innovation, not lag behind it by decades. Appealing to the automakers in attendance, Bozzella cited the success of voluntary commitments on automatic emergency braking as a model for future progress. “That’s a way to do something important and impactful ahead of regulation.” They advocated for shared data platforms, anonymous reporting, and a common regulatory vision that sets safety baselines while allowing room for experimentation. The 40,000 annual road fatalities demand urgency — what’s needed is a move away from tactical fixes and toward a systemic safety strategy. “Safety delayed is safety denied,” Rosekind stated. “Tell me how you’re going to improve safety. Let’s be explicit.”

Drawing inspiration from aviation’s exemplary safety record, Kathy Abbott, chief scientific and technical advisor for the Federal Aviation Administration, pointed to a culture of rigorous regulation, continuous improvement, and cross-sectoral data sharing. Aviation’s model, built on highly trained personnel and strict predictability standards, contrasts sharply with the fragmented approach in the automotive industry. The keynote emphasized that a foundation of safety culture — one that recognizes that technological ability alone isn’t justification for deployment — must guide the auto industry forward. Just as aviation doesn’t equate absence of failure with success, vehicle safety must be measured holistically and proactively.

With assistive and automated driving top of mind in the industry, Pete Bigelow of Automotive News offered a pragmatic diagnosis. With companies like Ford and Volkswagen stepping back from full autonomy projects like Argo AI, the industry is now focused on Level 2 and 3 technologies, which refer to assisted and automated driving, respectively. Tesla, GM, and Mercedes are experimenting with subscription models for driver assistance systems, yet consumer confusion remains high. JD Power reports that many drivers do not grasp the differences between L2 and L2+, or whether these technologies offer safety or convenience features. Safety benefits have yet to manifest in reduced traffic deaths, which have risen by 20 percent since 2020. The recurring challenge: L3 systems demand that human drivers take over during technical difficulties, despite driver disengagement being their primary benefit, potentially worsening outcomes. Bigelow cited a quote from Bryan Reimer as one of the best he’s received in his career: “Level 3 systems are an engineer’s dream and a plaintiff attorney’s next yacht,” highlighting the legal and design complexity of systems that demand handoffs between machine and human.

In terms of the impact of AI on the automotive industry, Mauricio Muñoz, senior research engineer at AI Sweden, underscored that despite AI’s transformative potential, the automotive industry cannot rely on general AI megatrends to solve domain-specific challenges. While landmark achievements like AlphaFold demonstrate AI’s prowess, automotive applications require domain expertise, data sovereignty, and targeted collaboration. Energy constraints, data firewalls, and the high costs of AI infrastructure all pose limitations, making it critical that companies fund purpose-driven research that can reduce costs and improve implementation fidelity. Muñoz warned that while excitement abounds — with some predicting artificial superintelligence by 2028 — real progress demands organizational alignment and a deep understanding of the automotive context, not just computational power.

Turning the focus to consumers, a collision repair panel drawing Richard Billyeald from Thatcham Research, Hami Ebrahimi from Caliber Collision, and Mike Nelson from Nelson Law explored the unintended consequences of vehicle technology advances: spiraling repair costs, labor shortages, and a lack of repairability standards. Panelists warned that even minor repairs for advanced vehicles now require costly and complex sensor recalibrations — compounded by inconsistent manufacturer guidance and no clear consumer alerts when systems are out of calibration. The panel called for greater standardization, consumer education, and repair-friendly design. As insurance premiums climb and more people forgo insurance claims, the lack of coordination between automakers, regulators, and service providers threatens consumer safety and undermines trust. The group warned that until Level 2 systems function reliably and affordably, moving toward Level 3 autonomy is premature and risky.

While the repair panel emphasized today’s urgent challenges, other speakers looked to the future. Honda’s Ryan Harty, for example, highlighted the company’s aggressive push toward sustainability and safety. Honda aims for zero environmental impact and zero traffic fatalities, with plans to be 100 percent electric by 2040 and to lead in energy storage and clean power integration. The company has developed tools to coach young drivers and is investing in charging infrastructure, grid-aware battery usage, and green hydrogen storage. “What consumers buy in the market dictates what the manufacturers make,” Harty noted, underscoring the importance of aligning product strategy with user demand and environmental responsibility. He stressed that manufacturers can only decarbonize as fast as the industry allows, and emphasized the need to shift from cost-based to life-cycle-based product strategies.

Finally, a panel involving Laura Chace of ITS America, Jon Demerly of Qualcomm, Brad Stertz of Audi/VW Group, and Anant Thaker of Aptiv covered the near-, mid-, and long-term future of vehicle technology. Panelists emphasized that consumer expectations, infrastructure investment, and regulatory modernization must evolve together. Despite record bicycle fatality rates and persistent distracted driving, features like school bus detection and stop sign alerts remain underutilized due to skepticism and cost. Panelists stressed that we must design systems for proactive safety rather than reactive response. The slow integration of digital infrastructure — sensors, edge computing, data analytics — stems not only from technical hurdles, but procurement and policy challenges as well. 

Reimer concluded the event by urging industry leaders to re-center the consumer in all conversations — from affordability to maintenance and repair. With the rising costs of ownership, growing gaps in trust in technology, and misalignment between innovation and consumer value, the future of mobility depends on rebuilding trust and reshaping industry economics. He called for global collaboration, greater standardization, and transparent innovation that consumers can understand and afford. He highlighted that global competitiveness and public safety both hang in the balance. As Reimer noted, “success will come through partnerships” — between industry, academia, and government — that work toward shared investment, cultural change, and a collective willingness to prioritize the public good.

© Photo: Kelly Davidson Studio

Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.

MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a novel design. While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.

But now, the engineers have made just such a dataset available to the public for the first time. Dubbed DrivAerNet++, the dataset encompasses more than 8,000 car designs, which the engineers generated based on the most common types of cars in the world today. Each design is represented in 3D form and includes information on the car’s aerodynamics — the way air would flow around a given design, based on simulations of fluid dynamics that the group carried out for each design.

Each of the dataset’s 8,000 designs is available in several representations, such as mesh, point cloud, or a simple list of the design’s parameters and dimensions. As such, the dataset can be used by different AI models that are tuned to process data in a particular modality.

DrivAerNet++ is the largest open-source dataset for car aerodynamics that has been developed to date. The engineers envision it being used as an extensive library of realistic car designs, with detailed aerodynamics data that can be used to quickly train any AI model. These models can then just as quickly generate novel designs that could potentially lead to more fuel-efficient cars and electric vehicles with longer range, in a fraction of the time that it takes the automotive industry today.

“This dataset lays the foundation for the next generation of AI applications in engineering, promoting efficient design processes, cutting R&D costs, and driving advancements toward a more sustainable automotive future,” says Mohamed Elrefaie, a mechanical engineering graduate student at MIT.

Elrefaie and his colleagues will present a paper detailing the new dataset, and AI methods that could be applied to it, at the NeurIPS conference in December. His co-authors are Faez Ahmed, assistant professor of mechanical engineering at MIT, along with Angela Dai, associate professor of computer science at the Technical University of Munich, and Florin Marar of BETA CAE Systems.

Filling the data gap

Ahmed leads the Design Computation and Digital Engineering Lab (DeCoDE) at MIT, where his group explores ways in which AI and machine-learning tools can be used to enhance the design of complex engineering systems and products, including car technology.

“Often when designing a car, the forward process is so expensive that manufacturers can only tweak a car a little bit from one version to the next,” Ahmed says. “But if you have larger datasets where you know the performance of each design, now you can train machine-learning models to iterate fast so you are more likely to get a better design.”

And speed, particularly for advancing car technology, is particularly pressing now.

“This is the best time for accelerating car innovations, as automobiles are one of the largest polluters in the world, and the faster we can shave off that contribution, the more we can help the climate,” Elrefaie says.

In looking at the process of new car design, the researchers found that, while there are AI models that could crank through many car designs to generate optimal designs, the car data that is actually available is limited. Some researchers had previously assembled small datasets of simulated car designs, while car manufacturers rarely release the specs of the actual designs they explore, test, and ultimately manufacture.

The team sought to fill the data gap, particularly with respect to a car’s aerodynamics, which plays a key role in setting the range of an electric vehicle, and the fuel efficiency of an internal combustion engine. The challenge, they realized, was in assembling a dataset of thousands of car designs, each of which is physically accurate in their function and form, without the benefit of physically testing and measuring their performance.

To build a dataset of car designs with physically accurate representations of their aerodynamics, the researchers started with several baseline 3D models that were provided by Audi and BMW in 2014. These models represent three major categories of passenger cars: fastback (sedans with a sloped back end), notchback (sedans or coupes with a slight dip in their rear profile) and estateback (such as station wagons with more blunt, flat backs). The baseline models are thought to bridge the gap between simple designs and more complicated proprietary designs, and have been used by other groups as a starting point for exploring new car designs.

Library of cars

In their new study, the team applied a morphing operation to each of the baseline car models. This operation systematically made a slight change to each of 26 parameters in a given car design, such as its length, underbody features, windshield slope, and wheel tread, which it then labeled as a distinct car design, which was then added to the growing dataset. Meanwhile, the team ran an optimization algorithm to ensure that each new design was indeed distinct, and not a copy of an already-generated design. They then translated each 3D design into different modalities, such that a given design can be represented as a mesh, a point cloud, or a list of dimensions and specs.

The researchers also ran complex, computational fluid dynamics simulations to calculate how air would flow around each generated car design. In the end, this effort produced more than 8,000 distinct, physically accurate 3D car forms, encompassing the most common types of passenger cars on the road today.

To produce this comprehensive dataset, the researchers spent over 3 million CPU hours using the MIT SuperCloud, and generated 39 terabytes of data. (For comparison, it’s estimated that the entire printed collection of the Library of Congress would amount to about 10 terabytes of data.)

The engineers say that researchers can now use the dataset to train a particular AI model. For instance, an AI model could be trained on a part of the dataset to learn car configurations that have certain desirable aerodynamics. Within seconds, the model could then generate a new car design with optimized aerodynamics, based on what it has learned from the dataset’s thousands of physically accurate designs.

The researchers say the dataset could also be used for the inverse goal. For instance, after training an AI model on the dataset, designers could feed the model a specific car design and have it quickly estimate the design’s aerodynamics, which can then be used to compute the car’s potential fuel efficiency or electric range — all without carrying out expensive building and testing of a physical car.

“What this dataset allows you to do is train generative AI models to do things in seconds rather than hours,” Ahmed says. “These models can help lower fuel consumption for internal combustion vehicles and increase the range of electric cars — ultimately paving the way for more sustainable, environmentally friendly vehicles.”

“The dataset is very comprehensive and consists of a diverse set of modalities that are valuable to understand both styling and performance,” says Yanxia Zhang, a senior machine learning research scientist at Toyota Research Institute, who was not involved in the study.

This work was supported, in part, by the German Academic Exchange Service and the Department of Mechanical Engineering at MIT.

© Credit: Courtesy of Mohamed Elrefaie